As described, the purpose of TGF module is to suppress noisy pixels and highlight the remaining co-salient pixels. So we add a novel contrastive loss that can promote the differences between noisy and co-salient pixels, which can help model inter-image separability. The existing contrastive learning methods (e.g. [1]}, [2]}) are a simple instance discrimination task. It treats each image as an individual instance, and the purpose is that each image can be well distinguished by the contrastive learning framework. Next, we will describe the way to construct positive and negative samples for CoSOD task.
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